Combine Two Pandas Dataframe Into One New

I have two Pandas DataFrames whose data from different sources, but both DataFrames have the same column names. When combined only one column will keep the name. Like this: speed_d

Solution 1:

Use pd.merge

In [406]: (pd.merge(speed_df, temp_df, how='outer', on='ts')
             .rename(columns={'val_x': 'speed','val_y': 'temp'})
             .sort_values(by='ts'))
Out[406]:
   ts  speed  temp
005.09.0114.08.042    NaN   7.0232.0   NaN
341.0   NaN

In [407]: (pd.merge(speed_df, temp_df, how='outer', on='ts')
             .rename(columns={'val_x': 'speed', 'val_y': 'temp'})
             .sort_values(by='ts').ffill())
Out[407]:
   ts  speed  temp
005.09.0114.08.0424.07.0232.07.0341.07.0

Solution 2:

Two main DataFrame options, one is pd.merge and the other is pd.fillna. Here is the code:

df = speed_df.merge(temp_df, how='outer', on='ts')
df = df.rename(columns=dict(val_x='speed', val_y='temp'))
df = df.sort_values('ts')
df.fillna(method='ffill')

Hope this would be helpful.

Thanks

Solution 3:

You need to do a left outer join using pandas.merge function

d = pd.merge(speed_df,temp_df,on='ts',how='outer').rename(columns=\
{'val_x':'speed','val_y':'temp'})
d = d.sort_values('ts')
d['speed']=d['speed'].fillna(4)
d['temp']=d['temp'].fillna(7)

That should return you this: